TNO Quantum provides generic software components aimed at facilitating the development of quantum applications.
The tno.quantum.ml.classifiers.vc
package provides a VariationalClassifier
class, which has been implemented
in accordance with the
scikit-learn estimator API.
This means that the classifier can be used as any other (binary and multiclass)
scikit-learn classifier and combined with transforms through
Pipelines.
In addition, the VariationalClassifier
makes use of
PyTorch tensors, optimizers, and loss
functions.
Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.
Documentation of the tno.quantum.ml.classifiers.vc
package can be found here.
Easily install the tno.quantum.ml.classifiers.vc
package using pip:
$ python -m pip install tno.quantum.ml.classifiers.vc
If you wish to run the tests you can use:
$ python -m pip install 'tno.quantum.ml.classifiers.vc[tests]'
Here's an example of how the VariationalClassifier
class can be used for
classification based on the
Iris dataset:
Note that tno.quantum.ml.datasets
is required for this example.
from tno.quantum.ml.classifiers.vc import VariationalClassifier
from tno.quantum.ml.datasets import get_iris_dataset
X_training, y_training, X_validation, y_validation = get_iris_dataset()
vc = VariationalClassifier()
vc = vc.fit(X_training, y_training)
predictions_validation = vc.predict(X_validation)